Systems and Methods For Analyzing Electronically Embodied Games

ABSTRACT

Features of electronically embodied games are logically categorized, analyzed, and compared. Features are preferably organized according to a hierarchical classification scheme, according to a classification scheme that is not strictly tautological. All suitable feature sets are contemplated, including sets corresponding to characteristics of players and non-players, types and/or uses of game space, methods of rewarding a player, etc. In other aspects comparisons are made between an evaluation game and one or more sets of historically available games. Such sets can be grouped by genre and the number of games in such sets can range anywhere from a single game to hundreds of games, or more. Reporting and guidance can include providing a risk assessment score or other risk analysis, feature assessment (prevalence), market placement, business model analysis, dynamic trend analysis, clustered pattern recognition, and image analysis.

This application claims priority to provisional application Ser. Nos.60/792,915 filed on Apr. 17, 2006, and 60/792,916 filed on Apr. 17,2006.

FIELD OF THE INVENTION

The field of the invention is electronic games.

BACKGROUND

All games, including video games, comprise sets of rules. Such rule setsdefine every-thing from the visuals of the environment, to the means bywhich a person or object moves through such an environment, manipulatesthat object or environment and, eventually, completes the game.

Significantly, no system has been developed for logically analyzing andcomparing video games on a feature by feature basis. In fact, suchanalysis has not even been possible because a standard nomenclature forfeatures of video game features has not been established. It is truethat games are routinely classified into genres, (e.g., Action,Fighting, Role-playing, Massively Multiplayer Online, Platform,Simulation, Sports, and Strategy), and are also routinely classifiedaccording to their age and skill levels. But such classifications aresubjective, and there appears to be no rigorous logical system formapping between feature sets and game classifications.

The closest prior art seems to be U.S. Publication No.: 2003/0065978(parameterizing errors of a software product already released to thepublic), U.S. Pat. No. 6,937,913 (parameterizing features of productsthat customers say they want or need), and U.S. Pat. No. 6,826,541(applying a heuristic to criteria identifiers of products). These andall other extrinsic materials discussed herein are incorporated byreference in their entirety. Where a definition or use of a term in anincorporated reference is inconsistent or contrary to the definition ofthat term provided herein, the definition of that term provided hereinapplies and the definition of that term in the reference does not apply.

Many problems in the video game design process result from this absenceof systems and methods for logically analyzing and comparing video gameson a feature by feature basis. For example, game production is oftenhindered because genre-standard features are often forgotten or areremembered late in the design process, only to be hastily implemented atthe last minute. On the flip side of the coin, non-standard features areoften given undo emphasis and allotted a disproportionate amount ofdevelopment resources. Additionally, without a standard for comparison,different departments within development studios and publishing housescan have difficulty even agreeing on what the core features of theirvideo game should be, or deciding what percent of their developmenteffort should be spent on innovation as opposed to delivering on coregenre features.

The problems extend to the investment side of gaming as well. Most gamedevelopers who are seeking funding are also in a difficult position whenattempting to convince investors that they have a product which can besuccessful in the marketplace. Many games which could have been verysuccessful never make it off the ground because the studios simply can'tsell their ideas effectively. From the investor's perspective, there isa total lack of tools and services which with the risk of the investmentcan be assessed. For the most part, investors must rely on experience,instinct, intuition and a great deal of luck.

Thus, there is still a need for systems and methods in which video andother electronically embodied games can be analyzed and comparedaccording to a features, with appropriate guidance provided tointerested parties.

SUMMARY OF THE INVENTION

According to the present invention, features of audiovisual and otherelectronically embodied games are logically categorized, analyzed, andcompared. Guidance is then preferably provided to interested parties,including for example development studios, publishers, marketing people,and investors.

In preferred embodiments, the features are organized according to ahierarchical classification scheme that is not strictly tautological.All suitable feature sets are contemplated, including, for example,feature sets corresponding to characteristics of player and non-playercharacters, types and/or uses of game space, and methods of rewarding aplayer. Relatively smaller features sets of at least 25 or 50 featuresare contemplated, but larger feature sets, such are 100, 250, or even1,000 members, are considered preferable because they provide a greaterlevel of granularity to the analysis.

In other aspects of preferred embodiments, comparisons can be madebetween an evaluation game and one or more sets of historicallyavailable games. Such sets can be grouped by genre and the number ofgames in such sets can range anywhere from a single game to hundreds ofgames, or more. Such set can also include successful and non-successfulgames, or combinations of these. Comparisons can be especially usefulwhere an evaluation game has not yet been widely marketed, which isdefined herein to mean games in which no more than 5,000 copies havebeen sold, excluding pre-sales, evaluations, contests and beta tests.

All commercial aspects of guidance are contemplated, including forexample, providing expectations with respect to income, sales volume,one or more temporally related price points, and number ofsubscriptions. Guidance can additionally or alternatively include one ormore of providing a risk assessment score or other risk analysis,feature assessment (prevalence), market placement, business modelanalysis, dynamic trend analysis, clustered pattern recognition, andimage analysis.

Various objects, features, aspects and advantages of the presentinvention will become more apparent from the following detaileddescription of preferred embodiments of the invention, along with theaccompanying drawings in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic of a prior art system used to rate aspects ofvideo games

FIG. 2 is a schematic showing analysis of existing games according to aclassification system, and analysis and guidance with respect to anevaluation game.

FIG. 3A is a tree hierarchy of a portion of an exemplary classificationsystem.

FIG. 3B is a more detailed representation of a portion of the treehierarchy of FIG. 3A.

FIG. 4 is a portion of an exemplary set of features derived from to ahypothetical evaluation game.

FIG. 5 is a high level perspective of steps involved in an exemplaryanalysis of the set of features of FIG. 4.

FIGS. 6A-6I are exemplary reports that could be generated from theanalysis of FIG. 5.

FIG. 7 is a schematic of providing guidance from the reports of FIGS.6A-6I.

FIGS. 8A-8G are sample reports derived from analysis of historicalgames.

DETAILED DESCRIPTION

In FIG. 1, the prior art system 100 used to rate aspects of video gamesgenerally depicts the games 110A, 110B, a rating system 120, result sets130A, 130B, feedback loop 162 and feedback recipients 190.

Games 110A, 110B can be any electronically embodied games, which term isused herein to mean games that are, or are intended to be, marketed orused at least in part in an electronic format. In common vernacular,such games are traditionally described in terms of the hardware used toaccess them. This includes, for example, video games which would begames played on a game console (e.g. a PlayStation™, a Wii™, an Xbox™),computer games which are games played on a personal computer, handheldgames which are games played on a hand-held gaming device other than aconsole (e.g. a GameBoy™, a PSP™, Nintendo™ DS™), mobile games which aregames played on cell phones and other small mobile devices, web basedgames, which are games played through or with the aid of a web browser(e.g, Microsoft's Internet Explorer™, and Mozilla™ Firefox™), and arcadegames which are games played on a large, stationary kiosk. It should beappreciated that the term “electronically embodied games” also includesgames that include real-world aspects, including for example,3-dimensional tokens, RFID and other cards, and so forth.

Rating system 120 includes any conventionally existing systems forrating electronically embodied games, whether such systems are intendedfor marketing or any other purposes. Exemplary conventional ratingsystems are ESRB, genre, platform (what hardware is the game played on,)hardware specifications (what is the minimum graphics card or processorspeed that is needed to play the game), publisher, developer, internetconnectivity (whether a player needs to be connected to the internet toplay the game), multiplayer (whether more than 1 person play the game atthe same time), etc. All of these known rating systems are quitesimplistic compared with some of the inventive ratings systems describedherein.

Results sets 130A, 130B are produced by the conventional rating systems120. Results sets 130A can be used by developers, publishers, marketingpersonnel, investors and other recipients 190 for their specificbusiness purposes, and other results sets 130B are used by consumers,parents, and others for consumer purposes. Such results sets 130A, 130Bare entirely conventional. For example, several websites provide basicoverviews of game ratings (http://gamespy.com/,http://www.gamespot.com/, and http://www.ign.com/), usually in the formof reviews of the games (e.g. “we gave it 4 of 5 stars”). Conventionalratings can also take the from of review articles, as can be seen athttp://ps2.gamespy.com/playstation-2/god-of-war/,http://www.gamespot.com/ps2/action/godofwar/index.html?q=god%20of%20war,and http://ps2.ign.com/objects/661/661321.html. Simplistic platform andgenre designations are also conventional, and are generally used assearch filters, for example. to show only action games for thePlayStation 3. Finally, results sets are sometimes used by groups suchas the ESA (Entertainment Software Association, http://www.theesa.com/)to analyze genre and market share, but these reports are not gamespecific. They are written about the state of the industry or the stateof the genre, as opposed to analysis of specific games.

Feedback 162 to developers and other has usually been limited toindividual research or canned reports. DFC Intelligence(http://www.dfcint.com/), for example, produces reports from time totime that provides analysis as to issues facing various segments of thegaming market. Seehttp://www.gamasutra.com/php-bin/news_index.php?story=13310.

Feedback recipients 190 are contemplated herein to include groups whocan afford to do or pay for basic research. These groups involve gamedevelopers (development studios), game publishers (publishers), videogame marketing and PR firms (marketing and PR), lawyers, and, recently,investment groups/fund who have been seeing video games as an emergingmarket worth investing in.

In FIG. 2, a system 200 generally comprises historically available games210A, an evaluation game 210B, a sophisticated classification system220, results sets 230A,B, analysis 240, reports 250, 270, and guidance260 to feedback recipients 290.

Historically available games 210A could be the same as games 110A, butcould also be a subset or superset of games 110A. There might beadvantages, for example, in marketing different analyses 240 and/orreports 250, 270 at different prices, depending upon the number ofhistorically available games considered. It is contemplated that aspecific evaluation game (or set of games) could be analyzed a large setof 20, 50, 100 or even more historically available games.

Evaluation game 210B would be any game being analyzed. It iscontemplated that in most instances the evaluation game 210B would besubmitted by someone eliciting analysis and reporting. But in otherinstances a game might be evaluated for the purposes of marketing aservice. Thus, some reports might be generated as a teaser to thepublisher or developer of a prospective or marketed game, to generateinterest in purchasing additional analysis or reports.

The classification system 220B preferably goes far beyond collecting afew features of a small set of games, and instead focuses on collectinga feature set that encompasses the breadth of all electronically games.Preferred embodiments include both broad categories (what environmentsdoes the game take place in, what is the mood and setting, what areelimination conditions for personifications), and more specificcategories such as the appearance customization choices that a gameplayer can make for their personification. In general, choice as toinclusion in the classification system revolves around deciding whatmight be useful knowledge for someone developing, publishing, marketingor investing in one or more game, companies in the games industry, oreven companies in a related industry. It is contemplated that theclassification system could change over time, possibly because theclassification is insufficiently detailed (e.g., we find that we need toinclude hair length as well as hair style), or because a feature turnsout to be irrelevant (i.e. nobody cares about hair color).

Preferred classification systems are also contemplated that go beyondmerely the presence of absence of a feature. Such systems canadvantageously classify the style of implementation andfrequency/quantity of such features. For example, instead of merelyclassifying a game as having a given level of violence, preferredclassification systems could qualify and/or quantify the instances ofviolence. Still further, systems are contemplated that classify howfeatures interact with each other. An example would be how violencerelates to weapons, use of automobiles or other transportation vehicles,or perhaps gender.

Especially preferred embodiments are hierarchical. For example, variousappearance customization choices (gender, body type, clothing selection,facial appearance, hair appearance, etc) can be further broken down intoattributes of that choice, (e.g. for hair appearance, one couldadvantageously classify the quantity and types of hairstyles availableas well as the range of color choices available). A current embodimentas 81 high level classifications, but could have a greater or lessernumber (e.g. 50-100) of such classifications. Embodiments arecontemplated that have at least 25, at least 50, at least 100, at least260, at least 1000 or even at least 10,000 members. Unless the contextdictates to the contrary, all ranges are inclusive of their endpoints.

Especially preferred embodiments are also not strictly tautological.Thus, a given feature could easily be represented in more than oneclassification. For example, red hair color of a character could beclassified under personal characteristics of the characters, and itcould also be classified as colors used prominently in the game.Similarly, there might be sub-classifications for red, blonde, and brownhair, but no separate classification for other.

Analysis 240 is predominantly or entirely mathematical, using regressionand/or other appropriate analytical tools. Analysis can compare successor other aspects of games against their own features, or against othersets of games. In the latter case, analysis is preferably made of anevaluation game against one or more relatively large sets ofhistorically available electronically embodied games. Such sets canadvantageously number at least 10, 25, 50, or 100 per genre grouping,which can include both successful and non-successful games. Ofparticular interest are situations in which the evaluation game has notyet been widely marketed, which is defined herein to mean games in whichno more than 5,000 copies have been sold, excluding pre-sells,evaluations, contests and beta tests.

Reports 250 can be generated using the analysis 240. Such reports caneither be delivered to one or more of the feedback recipients, with orwithout guidance 260 in interpreting the reports. Exemplary reports areshown and described herein, and include expected effects of including orexcluding particular features in a game, product uniqueness, and genresuitability.

Guidance 260 can include any useful information, such as providingexpectations with respect to income, sales volume, one or moretemporally related price points, and number of subscriptions. Guidancecan additionally or alternatively include one or more of providing arisk assessment score or other risk analysis, feature assessment(prevalence), market placement, business model analysis, dynamic trendanalysis, clustered pattern recognition, and image analysis.

Reports 270 can be generated directly from the result sets ofhistorically available games 230A. These reports do not compare a singlegame (evaluation game) against large groupings of historically availablegames, but instead compare large groupings of historically availablegames against each other. Exemplary reports 270 include video gametrends, forecasting, genre analysis, feature analysis, etc.

FIG. 3A is a tree hierarchy of a portion of an exemplary classificationsystem. In one aspect of preferred embodiments, the features areorganized according to a hierarchical classification scheme, and morepreferably according to a classification scheme that is not strictlytautological. Larger feature sets, at least 25, 50, 100, 260, or even1,000 members are generally considered preferable to smaller featuresets.

FIG. 3B is a more detailed representation of a portion (not shown) ofthe tree hierarchy of FIG. 3A. All suitable feature sets arecontemplated, including for example feature sets including subsetscorresponding to characteristics of a personification, types and/or usesof game space, and methods of rewarding a player.

FIG. 4 is a portion of an exemplary set of features derived from ahypothetical evaluation game. To facilitate the analysis 250 andreporting 250, 270, information from evaluation games is preferablyorganized using the same hierarchical classification system as used forhistorically available games.

FIG. 5 is a high level perspective of steps involved in an exemplaryanalysis 250. In this particular example, the evaluation result set 230Bis compared against the historical result set 230A to produce threecategories of results: (1) features present only in the evaluation game503; (2) features present in both the evaluation game and the historicaldata 504; and (3) features present only in the historical data 505. Oncedistinct sets of data have been identified 503,504,505, a secondaryanalysis of each result set is done against a number of criteria 506,depending on the final result sets desired to be created.Advantageously, this could include prevalence, risk, novelty, sales orcluster association. Most preferably it would include some or all ofthese in conjunction with one another for the most detailed reportingneeds. After the secondary analysis 506 has been performed numerous subresult sets are produced which contain the specific data desired fromthe analysis 507. This data 507 is then used to generate reports 250,270, or perhaps other reports.

FIGS. 6A-6C are exemplary reports that could be generated from theanalysis of FIG. 5.

FIG. 6A depicts an exemplary Feature Assessment (prevalence) report.Feature assessment reports are used to determine the prevalence offeatures in a genre or large grouping of games (as assessed fromhistorically available games). Such reports can be used on their own, orto provide guidance for a specific game (for example, the report mightshow a game designer that he is missing three features that greater thaneighty percent of his market competitors have, or that he implementedthese features in a way that is not in accordance with marketexpectations. Feature assessment can also be used to determine suchthings as common video game features (for example, these are the 100features that all games grossing greater than $1,000,000 had), todetermine base genre features (for example, these are the 40 featuresthat all shooter genre games grossing greater than $1,000,000 had), todetermine unsuitable base genre features (for example, these are the 40features that all shooter genre games grossing less than $200,000 had),to asses standard feature sets (for example, these are all the featuresof game A which X percent of other games in the same genre also had), orto asses unique features or features not commonly used by other games ofthe genre (for example, these are the features of game B which only Ypercent of the other games in the same genre also had).

FIG. 6B provides an exemplary Risk Assessment report, in this caseshowing numeric risk assessment scores. Risk assessment scores are basedon individual game features, or groupings/sets of such features, as wellas the implementation of the feature or feature set in a particulargame. For example, risk assessment scores can be a function ofoccurrence frequency and quantity of a given feature or feature set,and/or duration of the feature or feature set when compared to one ormore sets of historically available games. The sets can preferablyinclude any desired grouping, including for example all games in anindustry, a particular platform or genre, or a subset filtered accordingto one or more parameters (for example, only compared to all games thatgrosses >$1,000,000 dollars). These basic risk assessment scores can besupplemented by more specific risk assessment scores on a riskassessment index.

FIG. 6C shows a portion of an exemplary Market Placement report. Here,standard genre features and feature sets can be derived through analysisof historically available games.

FIG. 6D shows a portion of an exemplary Business Model Analysis report,which includes recommended and non-recommended game features. Byanalyzing specific evaluation games, the features of these games can beassessed to make sure that they fit the standard features of a targetmarket. Additionally, this feature/business model type of analysisallows game designers and publishers to shift their business model tomatch the business model of other successful games with similar featuresets. Alternately, games can be designed to have their features matchcommon features of other successful games of the business model they arepursuing and avoid features used by unsuccessful games of the businessmodel they are pursuing.

FIG. 6E shows a portion of an exemplary Dynamic Trend Analysis report.By analyzing the features and sets of features of video games inaccordance with their release dates, past trends in features and theirprevalence can be determined. Through past trend analysis, new trendscan be predicted. These trends can be filtered by one or more specificfeatures such video game genre, video game gross sales, etc. to providemore focused trend analysis (for example, in 2000, 55% of shooter gamesthe grossed greater than $20 million dollars had feature X, while in2001, the same feature showed up in 75% of all shooter games grossinggreater than $20 million).

FIG. 6F shows a portion of an exemplary Clustered Pattern Recognitionreport; By analysis of video game features and the genres they fallinto, new genres of games and new types of features can be identifiedbased on scatter graphs and other forms of pattern recognition. Cloudreports such as FIG. 6F visually represent either: every historical gameand its' overall position in relation to specified criteria (forexample, Genres); or every feature and its' overall position in relationto specified criteria. A cloud graph may have as few as 2 axis endpoints and as many as 1000, but most preferably between 4 and 10. Cloudgraphs are especially useful in visually identifying key elements,including for example:

-   -   Those games or features which are within a cloud, and therefore        can be clearly identified as belonging to a specific cloud        group;    -   Those games or features which are not within any specific cloud        at all, and are therefore breakouts from any specific grouping;    -   Those games or features which are at the head of a cloud and        could be part of a new trend to expand the cloud in a specific        direction;    -   Those games or features which are at the tail of a cloud and        could soon no longer be part of the main cloud.

It is also possible to graph a Cloud Report over time to generatemultiple versions of the same cloud report but using data from segmentedtime periods to see how the clouds change over that period of time andto therefore predict in what direction the clouds may move in thefuture. By visually comparing the historical data and with a proposedgame design, the cloud report can quickly identify positioning of theproposed game and its features.

FIG. 6G shows a portion of an exemplary Image Analysis Report. Byanalyzing pixels of an evaluation image, advantageously as promotionalimages related to the game, but most preferably as art used as part ofthe packaging of the game, including relationships between adjacent andnearby pixels and relationships of a given pixel or group of pixels ascompared to the totality of the image as a whole; data on the evaluationimage can be stored and compared with image data related to otherhistorically available images. The data on the evaluation image andhistorically available images can also be compared with other sets offeature data related to the game that the image represented,advantageously including success or categorizing data, most preferablygenre, ESRB ratings or sales, to form patterns and relationships whichcan identify relationships between image properties and game success ormarketing targets.

FIG. 6H shows a portion of an exemplary Feature vs. Game Income report.Here, features can be matched against game performance, as expressed bygross game income to assist determining feature suitability and gamerisk. Reports like this can be filtered by other features, such as gamegenre or release date. For example, the feature ESRB rating, filtered bygame genre, could be compared against game income, showing that TeenESRB rated shooter games are more consistent in their gross income,where Mature ESRB rated shooter games skew higher and lower on theirgross incoming, including more breakaway titles but also more flops.

FIG. 6I shows a portion of an exemplary Feature Value By Genre report.Here, genres having individual features (or groups of features) aregraphed against income and game release date. By analyzing individualfeatures and sets of features of a significant quantity (5, 10, 20, 50,100, 500+) of historically available games verses their gross incomes,the base dollar value of each feature and/or set of features can bedetermined. As new games are released, they can be analyzed to keep thedata current, while, if necessary, old games no longer deemed relevantcan be filtered out. Value of each feature or set of features can bedetermined for all games, or can be determined by genre (for example,feature X in genre A might be worth $20,000 in predictive sales, whilethe same feature X in genre B might be worth $50,000 in predictivesales).

FIG. 7 is a schematic 700 depicting aspects of extending from reports710 through an analysis 720 to provide guidance 730 to designers,publishers, marketers, investors, and possibly others. In some cased,reports 250 and 270 are given directly to feedback recipients 290. Inothers cases, guidance is given to the feedback recipients. In thisparticular instance, reports 710 include the examples of FIGS.250A-250I, but could include additional or alternative reports (notshown).

With respect to content, all commercial aspects of guidance arecontemplated, including for example, providing expectations with respectto income, sales volume, one or more temporally related price points,and number of subscriptions. Guidance can additionally or alternativelyinclude one or more of providing a risk assessment score or other riskanalysis, feature assessment prevalence, market placement, businessmodel analysis, dynamic trend analysis, clustered pattern recognition,and image analysis.

Guidance 730 can take any suitable form, including for example:

-   -   Consultations and conferences 730A. Here, feedback recipients        can receive individual consultations on the reports generated        for their evaluation game or can attend conferences where they        receive guidance advice/analysis of the reports and data        generated from large amounts of historically available games.    -   Recommendations 730B. Here, feedback recipients can receive        recommendations in written and/or graphic format.        Recommendations make use of the analysis and suggest a course of        action for the feedback recipients to take.    -   Risk analysis 730C. Here, feedback recipients can receive risk        analysis on their evaluation game. Risk analysis can be provided        in the form of a risk assessment score or other risk analysis;        based on the feature sets, implementation methodology as well as        feature occurrence frequency and quantity, when compared to        other similar historically available games, to provide basic        risk assessment or a specific risk assessment score on a risk        assessment index.    -   Analyzed reports 740D. Here, feedback recipients can receive        analyzed reports, providing both the reports with corresponding        numeric, graphic and written data and analysis of the reports        specific to evaluation games, genres, the industry or even        specific features or groups of features.

FIGS. 8A-8G are sample reports derived from analysis of historicalgames. Here, there is no comparison per se, against an evaluation game.Readers will appreciate that these reports are similar to several ofthose in FIGS. 6A-I.

Thus, specific embodiments and applications of systems & methods foranalyzing electronically embodied games have been disclosed. It shouldbe apparent, however, to those skilled in the art that many moremodifications besides those already described are possible withoutdeparting from the inventive concepts herein. The inventive subjectmatter, therefore, is not to be restricted except in the spirit of theappended claims. Moreover, in interpreting both the specification andthe claims, all terms should be interpreted in the broadest possiblemanner consistent with the context. In particular, the terms “comprises”and “comprising” should be interpreted as referring to elements,components, or steps in a non-exclusive manner, indicating that thereferenced elements, components, or steps may be present, or utilized,or combined with other elements, components, or steps that are notexpressly referenced. Where the specification claims refers to at leastone of something selected from the group consisting of A, B, C . . . andN, the text should be interpreted as requiring only one element from thegroup, not A plus N, or B plus N, etc.

1. A method, comprising: parameterizing a set of features of a firsthistorically available electronically embodied game, and correlatinginclusion of at least one member of the set of features with acommercial aspect of the first game; parameterizing the at least onemember with respect to an evaluation game; and providing a recipientwith guidance regarding inclusion of the at least one member withrespect to the evaluation game.
 2. The method of claim 1, wherein thestep of parameterizing comprises using a hierarchical classificationscheme.
 3. The method of claim 3, wherein the step of parameterizingcomprises using a classification scheme that is not strictlytautological.
 4. The method of claim 1, wherein the set of featuresincludes at least 100 members.
 5. The method of claim 1, wherein the atleast one member comprises a characteristic of a personification.
 6. Themethod of claim 1, wherein the at least one member comprises a type ofgame space.
 7. The method of claim 1, wherein the at least one membercomprises a method of rewarding a player.
 8. The method of claim 1,further comprising correlating inclusion of at the least one member withcommercial aspects of at least 25 other historically availableelectronically embodied games.
 9. The method of claim 8, wherein thecommercial aspect is selected from the list consisting of income andsales volume.
 10. The method of claim 1, wherein the commercial aspectis selected from the list consisting of at least two temporally relatedprice points, and number of subscriptions.
 11. The method of claim 1,wherein the first game is regarded as having been commerciallyunsuccessful.
 12. The method of claim 1, wherein the evaluation game hasnot yet been widely marketed.
 13. The method of claim 1, wherein therecipient comprises an investor.
 14. The method of claim 1, wherein theguidance comprises a successfulness risk analysis.
 15. The method ofclaim 1, wherein the guidance comprises a feature assessment
 17. Themethod of claim 1, wherein the guidance comprises a dynamic trendanalysis
 18. The method of claim 1, wherein the guidance comprises aproviding a risk assessment score.
 19. The method of claim 1, whereinthe guidance comprises a clustered pattern recognition.
 20. The methodof claim 1, wherein the guidance comprises an image analysis.